Basics of Prompts
Whether in coding, literature search, or data analysis, a well-crafted prompt improves the relevance, accuracy, and quality of the responses. Understanding prompt basics is essential for effectively using generative AI tools, as prompts are the instructions that guide these models to produce desired outputs. By mastering prompt basics, users can communicate more clearly with AI models, setting the foundation for more advanced techniques like prompt engineering, which fine-tunes prompts to achieve even more precise results.
What is a prompt?
A prompt is a piece of text or input given to an AI model to guide its response.
- Role in Communication: The prompt guides the AI by setting the tone, context, and direction of the response, ensuring the output aligns with the user’s goals.
- Variability: Prompts can vary in complexity, length, and style, depending on the desired result—ranging from simple questions to detailed instructions.
- Processing the Input: When a generative AI model receives a prompt, it parses the language to understand the request, identifying key elements and determining the response type, which helps the AI produce relevant and accurate outputs.
Main Types of Prompts
1. Informational Prompts
These prompts ask the AI to provide background information, summaries, or explanations on a specific topic. They are fact-oriented, seeking comprehensive explanations.
2. Instructional Prompts
Instructional prompts guide the AI to perform specific tasks or generate step-by-step outputs. They are task-focused, clear, with defined output goals.
3. Query Prompts
Query prompts are used to ask specific questions, often for fact-finding or clarifying details on particular concepts or methods. They are aimed at obtaining quick, straightforward answers.
Prompt Engineering
Prompt engineering is the practice of designing prompts strategically to achieve precise and relevant outputs from an AI model. By structuring prompts with clear instructions, relevant context, and guiding cues, users can enhance the quality and specificity of AI responses. In bioinformatics, this technique can be used to produce targeted outputs for coding, data analysis, literature summarization, and more.
Five Key Principles of Prompt Engineering:
- Giving Instructions
- Direct the Models Focus with Primary Content
- Providing Examples
- Priming the Output with Cues
- Evaluate Output Quality
1. Giving Clear Instructions
Instructions direct the AI model on what action to perform, from simple tasks to complex, multi-step requests. The level of detail in instructions impacts the model’s ability to meet specific needs.
Less Effective Prompt:
Summarize the meeting notes.
More Effective Prompt:
Summarize the meeting notes in a single paragraph. Then list the speakers with each of their key points. Finally, outline any next steps or action items suggested by the speakers.
2. Direct the Models Focus with Primary Content
Primary content specifies the main body of text the model is expected to process or transform. When combined with instructions, it helps the AI concentrate on the essential elements of the task.
3. Providing Examples
Examples improve the model’s response accuracy and relevance by setting a template for the desired output. They help ‘condition’ the AI, especially when demonstrating specific types of responses or styles.
Less Effective Prompt:
Create a comprehensive report on the implementation of a new electronic health records (EHR) system.
More Effective Prompt:
Create a comprehensive report on the implementation of a new electronic health records (EHR) system in a mid-sized hospital. For instance, describe how the project team managed budget constraints while ensuring system efficiency, similar to how the XYZ Hospital project was handled.”
4. Priming with Cues
Cues serve as starting points or ‘jumpstarts’ for the AI’s response, setting up the output structure. They help guide the model toward the desired format and flow.
Example Cue:
In 5 concise points, summarize…
5. Evaluate Output Quality
Assessing the AI’s output is crucial to ensure accuracy and usability. Key questions to consider include:
- Is the information correct and relevant?
- Is the output unbiased?
- Is the response clear and suitable for the intended purpose?
- Could refining the prompt improve the result?
Pulling It All Together
Combining all elements—clear instructions, focused content, examples, and cues—leads to a well-engineered prompt that yields high-quality output.
[Instructions] Create a comprehensive report on the implementation of a new electronic health records (EHR) system in a mid-sized hospital.
[Primary Content] Focus on key project management aspects such as timeline, budget, stakeholder engagement, and risk management.
[Examples] For instance, describe how the project team managed budget constraints while ensuring system efficiency, similar to how the XYZ Hospital project was handled.
[Cues] Start with an overview of the project scope and objectives.”
Advanced Prompting Techniques
Advanced prompting techniques allow users to communicate more effectively with AI models, leading to more accurate and tailored responses. These strategies can help ensure that the model processes complex requests accurately, provides clear reasoning, and handles repetitive tasks efficiently.
Give the Model a Role
Assigning a role to the model at the beginning of the prompt encourages it to respond in a manner consistent with that role. By setting a specific role, such as “data analyst,” “teacher,” or “research assistant,” the model can better align its responses with the expected expertise or perspective.
Specify Steps to Complete the Task
For tasks that require multiple actions, break down the process into clear, sequential steps. Using bullet points or numbered lists improves clarity, helping the model execute each step in the correct order.
Indicate Distinct Parts of Your Prompt
Dividing your prompt into sections or labeled parts helps the model focus on each component of the request. Adding section titles or ordered steps clarifies the output structure and ensures the response covers all aspects of the prompt.
Chain of Thought Prompting
Chain of Thought (CoT) prompting instructs the model to follow a logical, step-by-step reasoning process. This technique is particularly helpful for complex problem-solving tasks, as it allows the model to “think aloud” and process each step methodically, improving transparency and reducing errors.
Example CoT Prompts:
- “Explain this concept step-by-step.”
- “Break down the reasoning behind each answer.”
- “Imagine you’re teaching this to someone new. Explain each step in detail.”
- “Consider each part separately and explain your approach.”
- “What assumptions are being made? Walk me through your thought process.”
- “Explain this concept step-by-step.”
Get the Cleanest Responses
To maximize output quality, consider specifying the desired length and style of the response. Use concise language to avoid unnecessary verbosity, and remember token limits, which cap the length of the model’s response.
Tips for Clear Responses:
- Specify output length: “Summarize in three sentences.”
- Request concise responses: “Answer briefly.”
- Understand token limits to avoid overly long or cut-off responses.
- Specify output length: “Summarize in three sentences.”
Breaking Down Complex Tasks
Complex tasks are often best handled by breaking them into smaller, manageable steps. By structuring complex prompts into simpler stages, the model can focus on one part at a time, increasing accuracy and relevance.
Leveraging Templates for Repetitive Tasks
Using templates can help automate repetitive tasks by providing a consistent format. Templates standardize responses and save time, particularly when similar tasks must be performed multiple times.
Task: Summarize Email Email
Content: [Paste the entire email content here.]
Instructions:
- Provide a concise summary of the email.
- Highlight any key points or action items.
- Identify the sender’s primary intent or purpose.
- Note specific dates, deadlines, or important details mentioned.
- Mention any unclear points or ambiguities in the email.
Supporting Information: [Add any context that may assist in understanding, such as previous related emails or project background.]
Using Generative AI Tools in Research
Generative AI, a branch of artificial intelligence, focuses on creating algorithms capable of generating new data that resembles existing information. This technology is powered by models such as large language models (LLMs) and other deep learning architectures, which can produce text, code, images, and even complex data predictions. With the advancement of generative AI, these tools are now being applied across various research fields, with bioinformatics emerging as one of the areas where they hold transformative potential.
Generative AI tools can streamline bioinformatics processes, making them faster and often more intuitive. For instance, generative AI can assist in automating coding tasks, searching through extensive literature, and identifying patterns within large biological datasets.
Key benefits of generative AI in bioinformatics include:
- Enhanced Literature Discovery: Streamlines finding relevant studies and identifying research trends.
- Coding Efficiency and Automation: Simplifies code generation, debugging, and repetitive tasks.
- Simplified Data Analysis: Facilitates complex data processing and statistical analysis.
- Visualization and Reporting: Enables creation of visuals and reports without extensive coding expertise.
Literature Search using Generative AI Tools
This section explores how generative AI tools like ResearchRabbit, SCISPACE, Elicit, Litmaps, and Perplexity can streamline the literature search process. These tools help researchers discover relevant studies, track new publications, map connections between topics, and summarize findings with ease. By harnessing AI to organize and synthesize information, researchers in bioinformatics and other fields can conduct thorough literature reviews more efficiently, staying up-to-date with the latest research while focusing on the most relevant content.
ResearchRabbit
ResearchRabbit is a free and powerful AI-driven literature search and discovery tool designed to help researchers explore and organize academic literature more effectively. It connects users to vast databases across multiple fields, particularly excelling in the Biomedical and Life Sciences with access to PubMed as well as all subject areas through Semantic Scholar. ResearchRabbit covers hundreds of millions of academic articles, making it one of the largest literature databases available, second only to Google Scholar.
Literature Discovery
ResearchRabbit makes it easy to find relevant research papers by allowing users to start with a title, Digital Object Identifier (DOI), PubMed Identifier (PMID), keywords, or by connecting to a Zotero collection. Users can also upload a BibTeX or RIS file containing selected papers to begin their search. ResearchRabbit then builds a network of related studies, streamlining discovery and uncovering connections between publications that might not be immediately obvious. Users can further refine their collection by exploring similar studies, earlier foundational work, more recent developments, or filtering by specific authors. This ensures more targeted and relevant results in vast fields of study.
Visualization
ResearchRabbit offers a unique visual representation of search results through an interactive, graph-based layout. This feature helps users understand relationships between studies, identify influential works, and uncover new connections. Users can toggle between a network view and a timeline view to visualize links between their collection and up to 50 related papers. Labels can be customized to display either the first or last author alongside the publication year, enhancing clarity in exploring academic networks.
Personalized Collections
Researchers can create customized collections for different topics, projects, or themes. These collections help in organizing literature in a personalized library that is easy to revisit and expand.
Collaboration
ResearchRabbit allows users to share collections with colleagues, making it easier to collaborate on literature reviews or research projects. This feature enhances team-based work by ensuring everyone has access to the same resources and updates.
Alerts and Updates
Once a user has created a collection, ResearchRabbit will send alerts for newly published studies that are relevant to the topics of interest. This automated alert system ensures researchers stay up-to-date without constantly monitoring databases.
Recommendations
ResearchRabbit uses AI-driven recommendations to suggest additional papers, authors, or topics related to the user’s area of interest. These recommendations help expand literature searches, leading to a more comprehensive understanding of the field.
Challenge
Create a ResearchRabbit collection on the topic “Normalization methods for single cell RNA sequencing data”.
Example ResearchRabbit Collection:
Shareable link: https://www.researchrabbitapp.com/collection/public/NLVW23GMZP
SCISPACE
SCISPACE is an AI-powered platform designed to streamline and enhance the research process by helping users read, interpret, and synthesize complex academic literature. It serves as an all-in-one tool for everything from understanding difficult concepts to generating citations and even creating summaries or paraphrases. It helps researchers quickly summarize, analyze, and interpret complex academic papers, making it easier to digest detailed information and access context-specific explanations using the chat with PDF feature. SCISPACE is especially beneficial for researchers in bioinformatics, where papers often contain complex data, specialized terminology, and dense information.
Chat with PDF
SCISPACE includes an interactive “Chat with PDF” feature, enabling users to ask questions directly about the content of a PDF document. This feature provides immediate, specific answers from the document, facilitating faster comprehension and targeted information retrieval without needing to read the entire paper.
Co-Pilot for Research
SCISPACE functions as a real-time research assistant or “co-pilot,” guiding users through papers by providing explanations, answering questions, and breaking down complex sections. This feature allows users to engage more deeply with the content without extensive background knowledge.
Literature Review Assistance
SCISPACE helps researchers conduct comprehensive literature reviews by quickly summarizing key findings, identifying relevant studies, and suggesting connected works. This feature is ideal for staying up-to-date on recent advances or exploring a new area of study.
AI Writer
This feature assists researchers in generating well-structured, accurate content, whether for drafting sections of a paper, summarizing results, or creating reports. It saves time by producing high-quality text aligned with academic standards.
Find Concepts
SCISPACE can detect and explain core concepts within a document, providing definitions and context that help users understand complex terminology and subject-specific language, particularly useful for interdisciplinary research.
Paraphraser
The paraphrasing tool allows users to rephrase sentences or sections, maintaining the original meaning while avoiding direct quotation. This feature is useful for integrating findings from other sources in a unique way without risking plagiarism.
